Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions:The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Median rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

Amsterdam is set to maintain its position as Europe's most expensive city for apartment rentals in 2025, with median costs reaching 2,500 euros per month for a furnished one-bedroom unit. This figure is double the rent in Prague and significantly higher than other major European capitals like Paris, Berlin, and Madrid. The stark difference in rental costs across European cities reflects broader economic trends, housing policies, and the complex interplay between supply and demand in urban centers. Factors driving rental costs across Europe The disparity in rental prices across European cities can be attributed to various factors. In countries like Switzerland, Germany, and Austria, a higher proportion of the population lives in rental housing. This trend contributes to increased demand and potentially higher living costs in these nations. Conversely, many Eastern and Southern European countries have homeownership rates exceeding 90 percent, which may help keep rental prices lower in those regions. Housing affordability and market dynamics The relationship between housing prices and rental rates varies significantly across Europe. As of 2024, countries like Turkey, Iceland, Portugal, and Hungary had the highest house price to rent ratio indices. This indicates a widening gap between property values and rental costs since 2015. The affordability of homeownership versus renting differs greatly among European nations, with some countries experiencing rapid increases in property values that outpace rental growth. These market dynamics influence rental costs and contribute to the diverse rental landscape observed across European cities.

Rents in Germany continued to increase in all seven major cities in 2024. The average rent per square meter in Munich was approximately **** euros — the highest in the country. Conversely, Düsseldorf had the most affordable rent, at approximately **** euros per square meter. But how does renting compare to buying? According to the house price to rent ratio, house prices in Germany have risen faster than rents, making renting more affordable than buying. Affordability of housing in Germany In 2023, Germany was among the European countries with a relatively high house price to income ratio in Europe. The indicator compares the affordability of housing across OECD countries and is calculated as the nominal house prices divided by nominal disposable income per head, with 2015 chosen as a base year. Between 2012 and 2022, property prices in the country rose much faster than income, with the house price to income index peaking at *** index points at the beginning of 2022. Slower house price growth in the following years has led to the index declining, as incomes catch up. Nevertheless, homebuyers in 2024 faced significantly higher mortgage interest rates, contributing to a higher final cost. How much does buying a property in Germany cost? Just as with renting, Munich was the most expensive city for newly built apartments. In 2024, the cost per square meter in Munich was almost ***** euros pricier than in the runner-up city, Frankfurt. Detached and semi-detached houses are usually more expensive. The price gap between Munich and the second most expensive city, Stuttgart, was nearly ***** euros per square meter.

This map shows housing costs as a percentage of household income. Severe housing cost burden is described as when over 50% of income in a household is spent on housing costs. For renters it is over 50% of household income going towards gross rent (contract rent plus tenant-paid utilities). Miami, Florida accounts for the having the highest population of renters with severe housing burden costs.The map's topic is shown by tract and county centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. Current Vintage: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The Aosta Valley region had the highest average rent for residential real estate in Italy in 2023. In October that year, the square meter rent in Aosta Valley amounted to 20.6 euros, almost eight euros above the national average. The regions of Lombardy and Tuscany followed with an average price amounting to 17.7 and 16.3 euros per square meter respectively. The average rent in Italy has increased notably since before the COVID-19 pandemic, when it was below 10 euros per square meter.

The Balearic Islands, Catalonia, and the Community of Madrid were the most expensive Spanish regions for residential real estate rents in October 2023. The average monthly rent per square meter in these regions was 16.1 euros. On the other end of the scale stood regions such as Extremadura and Castile-La Mancha which had the most affordable rental housing. In Spain, the majority of households live in an owner-occupied home. Nevertheless, rental rates have grown substantially since 2013, showing that the market is growing.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

In 2022, Altstadt-Lehel was the most expensive district for rent in Munich, Germany, with an average monthly rent of about 23 euros per square meter of living space (excluding extra costs). In comparison, some of the more affordable districts, such as Feldmoching-Hasenbergl and Aubing-Lochhausen-Langwied, had average rents of under 17 euros per square meter.

Zoopla Properties Listing dataset to explore detailed property information, including pricing, location, and features. Popular use cases include real estate market analysis, property valuation, and investment research.

Use our Zoopla Properties Listing Information dataset to explore detailed property listings, including property details, pricing, location, and market trends across various regions. This dataset provides valuable insights into property valuations, consumer preferences, and real estate dynamics, enabling businesses and researchers to make data-driven decisions.

Tailored for real estate professionals, investors, and market analysts, this dataset supports market trend analysis, property valuation assessments, and investment strategy development. Whether you're evaluating property investments, tracking market conditions, or conducting competitive analysis, the Zoopla Properties Listing Information dataset is a key resource for navigating the real estate landscape.

Dataset Features

• url: The original listing URL on Zoopla.
• property_type: Type of property (e.g., Flat, Detached, Terraced).
• property_title: Title or headline of the listing.
• address: Full postal address of the property.
• google_map_location: Geographical coordinates (latitude, longitude).
• virtual_tour: Link to a virtual walkthrough or 360° tour.
• street_view: Link to the Google Street View of the property.
• url_property: Zoopla-specific property page URL.
• currency: Currency in which the property is priced.
• deposit: Security deposit required (typically for rentals).
• letting_arrangements: Letting details (e.g., short-term, long-term).
• breadcrumbs: Category breadcrumbs for location and type navigation.
• availability: Availability status (e.g., Available now, Under offer).
• commonhold_details: Information about commonhold ownership.
• service_charge: Annual service charge (for leasehold properties).
• ground_rent: Annual ground rent cost.
• time_remaining_on_lease: Lease duration remaining in years.
• ecp_rating: Energy Performance Certificate rating.
• council_tax_band: Council tax band.
• price_per_size: Price per square meter or foot.
• tenure: Tenure type (Freehold, Leasehold, etc.).
• tags: Descriptive tags (e.g., New build, Chain-free).
• features: List of property features (e.g., garden, garage, en-suite).
• property_images: URLs to property photos.
• additional_links: Other related links (e.g., brochures, agents).
• listing_history: Changes in price, listing dates, and status over time.
• agent_details: Information about the listing agent or agency.
• points_ofInterest: Nearby landmarks or facilities (schools, transport).
• bedrooms Number of bedrooms.
• price: Listed price of the property.
• bathrooms: Number of bathrooms.
• receptions: Number of reception rooms (living, dining, etc.).
• country_code: Country code of the listing (e.g., GB for UK).
• energy_performance_certificate: Detailed EPC documentation or summary.
• floor_plans: URL or data related to property floor plans.
• description: Detailed property description from the listing.
• price_per_time: Price frequency for rentals (e.g., per week, per month).
• property_size: Area of the property (in sq ft or sq m).
• market_stats_last_12_months: Market stats for the area over the past year.
• market_stats_renta_opportunities: Data on rental yields and opportunities.
• market_stats_recent_sales_nearby: Sales history for nearby properties.
• market_stats_rental_activity: Local rental activity trends.
• uprn: Unique Property Reference Number for UK properties.
• listing_label: Label/category of the listing.

Distribution

• Data Volume: 44 Columns and 95.92K Rows
• Format: CSV

Usage

This dataset is ideal for a variety of high-impact applications:

• Property Valuation Models: Train ML models to estimate market value using features like size, location, and amenities.
• Real Estate Market Analysis: Identify pricing trends, demand patterns, and neighbourhood growth over time.
• Investment Research: Analyse rental yields, price per square foot, and historical price changes for investment opportunities.
• Recommendation Systems: Develop intelligent recommendation engines for property buyers and renters.
• Urban Planning & Policy Making: Use location and infrastructure data to guide city development.
• Sentiment & Description Analysis: NLP-driven insights from listing descriptions and agent narratives.

Coverage

• Geographic Coverage: Global
• Time Range: Ongoing collection; historical data may span multiple years

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   -

This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25070, B25091 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

D.C.'s median rent for a one bedroom apartment stands at $2,495, significantly higher than the national median rent of approximately $1,567. Click on different U.S. cities to see the median rent for a one bedroom apartment2.The map on the left side shows the percentage of people by census tract that are considered "cost burdened" by housing costs, by paying 30% or more of their household income on rent and utilities3. The map on the right side shows the median household income by census tract4. You can click on the "list" icon in the lower left corner to see the map legend, and meanings of map symbology. Areas that are cost burdened are often areas with the lowest median household incomes. There are also areas in wards where median incomes are high, but the cost of living is also high, leading to a greater cost burden.

Which Twin Cities Metro census tracts are cost-burdened? The U.S. Department of Housing and Urban Development states that housing is “affordable” if no more than 30% of a household’s monthly income is needed for rent, mortgage payments and utilities. Households who pay more than 30% of their income on housing costs are considered cost-burdened.This map shows Median Gross Rent as a percentage of Median Household Income for Renters. Click on the census tracts to see the percentage, as well as Monthly Median Household Income for Renters and Median Gross Rent for that area.Source: American Community Survey, 2013 5-year estimates, Tables B25064 (Median Gross Rent), B25119 (Median Household Income by Tenure).Map made by CURA staff, Feb 2015.

New Zonage “A/B/C” applicable from 01/10/2014 (Ministerial Decree of 01 August 2014). The “A/B/C” zoning, created in 2003 at the time when Robien’s rental investment scheme was introduced, characterises the tension of the local real estate market, i.e. the adequacy of the demand for and the supply of available housing on a territory. It consists of five modalities ranging from the most tense (Abis) to the most relaxed (C).Franche-Comté is only affected by zones B2 and C. Several financial schemes use this zoning to determine the eligibility of territories for aid or to adjust their parameters (level of aid, ceiling of rents, etc.). These include the Intermediate Rental Investment Facility for Individuals (see Duflot Zoning), the Old Borloo, the Intermediate Rental Loan (PLI), the Zero Rate Loan (PTZ), the Social Accession Rental Loan (PSLA) and the Social Access Loan (PAS) to property, and the reduced rate VAT in the ANRU area.Some ANAH aid to social lenders is also linked to a ceiling on rent and the amount of resources of the tenant, which varies according to the zoning A/B/C. Following a consultation conducted by the Regional Prefect with the local authorities in the 4th quarter of 2013, the new zoning A/B/C was adopted by the Minister in charge of Housing on 1 August 2014. For Franche-Comté, 19 new municipalities were reclassified from C to B2, while no decommissioning was recorded. Its entry into force varies between 1 October 2014 and 1 February 2015 depending on the arrangements attached to it: as of 1 October 2014 for: — the zero-rate loan; — the guarantee scheme of the FGAS; — the reduced rate VAT scheme for intermediate rental accommodation (279-0a A of the CGI); — the aid scheme for intermediate rental investment for private individuals (199 novitiies of the General Tax Code (CGI); — promises of sales of public land, pursuant to Article R. 3211-15 of the General Code of Ownership of Public Persons; on 1 January 2015 for: — the benefit of aid from the National Housing Agency, the ‘old Borloo’ tax scheme; — the intermediate rental loan; — reduced VAT in ANRU area; — devices related to HLM promotion; — the assessment of resources for new intermediate dwellings held by HLML bodies in the context of their service of general economic interest; as of 1 February 2015 for: — approvals of social loans for leasing-accession. Data sources: order of the Minister of Housing dated 01 August 2014

In 2006, the average monthly shelter cost for renter households was $728 and for owner households it was $998. For owners without a mortgage it was lower at $455 and for owners with a mortgage it was higher at $1393. Shelter costs are the average monthly total of all shelter expenses paid by households to secure shelter. Shelter costs for owners may include the mortgage payment, property taxes, condominium fees and the costs of electricity, heat and municipal services. Shelter costs for renters may include rent and the costs of electricity, heat and municipal services. The map shows by census subdivision the average owner’s major (monthly) payments on shelter costs for households with a mortgage.

The highest cost of renting an apartment, regardless of its size, was recorded in Warsaw, Poland, as of the fourth quarter of 2023.

In 2022, Berlin Mitte was the most expensive district for apartment rentals, with an average asking basic rent of 14.5 euros per square meter (excluding extra costs). The average for the city in this period was 11.54 euros per square meter.

This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer). This layer is symbolized by the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25002, B25003, B25058, B25077, B25057, B25059, B25076, B25078Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Rent prices per square meter in the largest Dutch cities have been on an upward trend after a slight decline in 2020. Amsterdam remained the most expensive city to live in, averaging a monthly rent of 27.6 euros per square meter for residential real estate in the private rental sector. Monthly rents in Utrecht were around six euros cheaper per square meter. Both cities were above the average rent price of residential property in the Netherlands overall, whereas Rotterdam and The Hague were slightly below that. Buying versus renting, what do the Dutch prefer? The Netherlands is one of Europe’s leading countries when it comes to homeownership, having funded this with a mortgage. In 2023, around 60 percent of people living in the Netherlands were homeowners with a mortgage. This is because Dutch homeowners were able to for many years to deduct interest paid from pre-tax income (a system known in the Netherlands as hypotheekrenteaftrek). This resulted in the Netherlands having one of the largest mortgage debts across the European continent. Total mortgage debt of Dutch households reached a value of approximately 803 billion euros in 2023. Is the Dutch housing market overheating? There are several indicators for the Netherlands that allow to investigate whether the housing market is overheating or not. House price indices corrected for inflation in the Netherlands suggest, for example, that prices have declined since 2022. The Netherlands’ house-price-to-rent-ratio, on the other hand, has exceeded the pre-crisis level in 2019. These figures, however, are believed to be significantly higher for cities like Amsterdam, as it was suggested for a long time that the prices of owner-occupied houses were increasing faster than rents in the private rental sector.

Geneva, Switzerland, was the most expensive city to buy an apartment in Europe in the first quarter of 2024. The square meter price in Geneva was nearly 15,650 euros in that quarter, about 2,000 euros higher than the second city in the ranking, Zurich. Cost of rent Rents across the major cities in Europe increased significantly in 2023. One of the main factors driving high rents across European cities is the same as any other consumer-driven business. If demand outweighs supply, prices will inflate. The drive for high paid professionals to be located centrally in prime locations, mixed with the low levels of available space, high land, and construction costs, all help keep rental prices increasing. Mortgage rates The average mortgage interest rates across Europe in 2023 were all under five percent, except in Czechia, Romania, Hungary, and Poland. On an individual level, a difference of one percent would most likely mean thousands of euros in interest on the mortgage a person is paying, making timing key in house purchasing. Mortgage interest rates tend to be lower in Nordic countries due to the financial stability and reliability of its borrowers. Other factors that influence the mortgage interest rates include inflation, economic growth, monetary policies, the bond market and the overall conditions of the housing market. More stable markets also tend to have higher average prices.